Research Design+Stats- Exam Qs

Which of the following is NOT a disadvantage of a repeated measures design? Select one: A.multicollinearity B.autocorrelation C.practice effects D.carryover effects

Correct Answer is: A A "repeated measures" design, sometimes referred to as a "within-subjects design," uses more than one measurement of a given variable for each subject. For example, longitudinal studies and pre-test/post-test designs measure the same subjects multiple times. These designs have several disadvantages including: "Autocorrelation"*, which means that observations obtained close together in time from the same subjects tend to be highly correlated. This violates the independence of observations assumption made by statistical tests. "Practice effects"*, "carryover effects"*, and "order effects" all refer to systematic changes in subjects' performance due to prior exposure to a treatment condition or measurement (* incorrect options) . However, multicollinearity refers to a problem associated with multiple regression which occurs when two or more predictors are highly correlated with each other.

Excessive variability in a behavior over time can make it difficult to obtain accurate information about the effects of an intervention on that behavior. Such variability poses the biggest threat for which of the following research designs? Select one: A.single-subject B.factorial C.split-plot D.Solomon four-group

Correct Answer is: A In a single-subject research design, the target behavior is measured at regular intervals throughout the baseline and treatment phases. If the behavior changes often in strength, intensity, or frequency, it would be difficult to obtain a clear baseline reading or to determine if the intervention is having the desired effect.

uring a research study the participants are able to guess the research hypothesis, causing them to behave differently than they would under normal conditions. This phenomenon is due to: Select one: A.demand characteristics B.the Hawthorne effect C.the use of a quasi-experimental design D.the use of psychic research participants

Correct Answer is: A Demand characteristics are cues in a research study that allow participants to guess the hypothesis. As a result, participants may behave differently than they would under normal conditions. the Hawthorne effect The Hawthorne effect is a similar phenomenon, but refers to the tendency of research participants to behave differently due to the mere fact they are participating in research - rather than due to cues about how they are expected to behave. the use of a quasi-experimental design Quasi-experimental designs are simply designs which do not randomly assign participants to groups. the use of psychic research participants Finally, this choice is a possible, but less probable, cause of this phenomenon.

Different regression line slopes in a scatterplot suggests: Select one: A.differential validity B.a lack of factorial validity C.divergent validity D.a lack of convergent validity

Correct Answer is: A The slope of a regression line for a test is directly related to the test's criterion-related validity: The steeper the slope, the greater the validity. A test has differential validity when it has different validity coefficients for different groups, which is what is suggested by different regression line slopes in a scatterplot. Factorial validity refers to the extent a test or test item correlates with factors expected to be correlated with in a factor analysis. The extent a test does not correlate with measures of an unrelated construct is referred to as divergent validity. Convergent validity refers to the degree a test correlates with measures of the same or a similar construct.

If you wanted to compare the average depression level (as measured by the number of scored responses given on a depression inventory) of anorexic females to that of non-anorexic females, you would use which of the following statistical tests? Select one: A.two-way ANOVA B.student's t-test C.chi-square D.Kolmogorov

Correct Answer is: B The t-test (which is also known as student's t-test) is the appropriate statistical test to use when comparing two means. A two-way ANOVA would be used to compare means from a study with two independent variables; in this case, there is only one independent variable (diagnosis) with two levels (anorexic vs. non-anorexic). A chi-square test is used when the data from a study is frequency of observations within categories, as opposed to (as in this case) mean scores of groups. Finally, the Kolmogorov, an infrequently used test, is used with ordinal data (e.g., ranks).

All of the following are assumptions of the regression equation, except: Select one: A.a linear relationship exists between X and Y. B.the variability of Y scores is equal throughout the range of X scores. C.one can predict scores on Y on the basis of scores on X. D.changes in the level of X cause changes in the level of Y.

Correct Answer is: D A regression equation is used to predict the value of a Y variable on the basis of a person's score on an X variable. For example, if an industrial psychologist wanted to use a job applicant's score on a job selection test to predict his future score on a supervisor's rating scale, he could develop and use a regression equation to do so. When regression is used, there is not necessarily the assumption that changes in the value of X cause changes in Y. The X and Y variables are correlated, but a correlation between two variables does not always mean that they are causally related. The incorrect choices are all assumptions of the use of regression. a linear relationship exists between X and Y. The technique is based on the assumption that the relationship between X and Y can be depicted as a straight line. the variability of Y scores is equal throughout the range of X scores. This choice is referred to as the assumption of homoscedasticity. one can predict scores on Y on the basis of scores on X. And this describes the whole purpose of using regression.

According to the central limit theorem, Select one: A.as sample size increases, the shape of a sample distribution becomes more normal. B.as the size of a sampling distribution of means increases, its distribution becomes more normal. C.as sample size increases, the shape of a sampling distribution of means becomes more normal. D.as sample size increases, the shape of a sampling distribution of means approximates the shape of the population distribution.

Correct Answer is: C According to the central limit theorem, the shape of a sampling distribution of means approaches normality as sample size increases. The central limit theorem is covered in the Advanced Statistics section of your materials, and you should study it after you have a reasonably solid grasp of the material presented in the rest of the section.

A researcher wants to use age at onset of symptoms, global assessment of functioning score, and number of relatives who have received a diagnosis of a psychotic disorder to predict whether patients with schizophrenia are at risk or not at risk for relapse following their first hospitalization. The appropriate technique is: A. canonical correlation B. multiple regression analysis C. discriminant function analysis D. analysis of covariance (ANCOVA)

Correct Answer: C it's correlational bc predictor and criteria - includes a single criterion (status in terms of relapse) and measured on a nominal scale (at risk or not at risk) --> so you use discriminant function analysis because criterion is measured on nominal scale **canonical correlation and multiple regression analysis are for criterion measured on interval or ratio scale

A hospital wishes to improve the effectiveness of a 6-week inpatient program to treat alcohol abuse by adding a half-year outpatient aftercare program. To evaluate the new program, a counselor will conduct 12-month follow-up interviews with the former inpatients. This hospital researcher would use multiple regression to: A.identify the homogeneous subgroups of patients based on response to treatment. B.discover what aftercare contributes above and beyond the inpatient program. C.test hypotheses about latent variables that influence response vulnerability. D.identify a taxonomy of types of alcohol abuse.

Correct answer is: B.discover what aftercare contributes above and beyond the inpatient program.

In an ABAB design: Select one: A.the same subject is administered all treatments. B.different subjects are administered treatments. C.a treatment is administered to one subject across a number of different settings. D.a treatment is administered to the same subject for a number of different behaviors.

Correct Answer is: A An ABAB design is a type of single-subject design. It is an example of a reversal design -- a baseline measure of a behavior is obtained (the "A" phase), the behavior is again measured after a treatment is administered (the "B" phase), the treatment is removed or reversed and the behavior is again measured, (the second "A"), and the behavior is again measured after the treatment is re-applied (the second "B"). In other words, the same subject receives all the treatments that are applied (actually, the same treatment at different times; thus, the word "all" might be somewhat misleading, but this is still the best answer). a treatment is administered to one subject across a number of different settings. a treatment is administered to the same subject for a number of different behaviors. These two choices are examples of multiple baseline designs.

One potential advantage of nonparametric statistical tests over parametric tests is that the former A.require fewer assumptions about the population data. B.are more powerful. C.result in a lower probability of false positives. D.provide more precise information.

Correct Answer is: A Both parametric and nonparametric tests are used in statistical hypothesis testing. In both, sample data is collected and analysis is run to see if the data supports a research hypothesis. Parametric tests make assumptions about the underlying population data (e.g., that data is normally distributed) and also are typically used to estimate population parameters. For instance, given a statistic from a sample of subjects, parametric tests typically indicate the probability that the statistic falls within a certain range in the underlying population. By contrast, nonparametric tests do not make assumptions about and do not attempt to estimate population parameters. Another advantage of nonparametric tests is they can be used to test hypotheses about ranked data or non-numerical categorical data. However, when the assumptions required to use them are met, parametric tests provide more accurate and precise results than nonparametric tests.

Which of the following best describes confidence intervals use? Select one: A.estimate true scores from obtained scores B.calculate the standard error of measurement C.calculate the test's mean D.calculate the standard deviation

Correct Answer is: A Confidence intervals allow us to determine the range within which an examinee's true score on a test is likely to fall, given his or her obtained score. The standard error of measurement is used to construct confidence intervals, not the other way around.

A within-subjects design that involves changing the order in which each treatment is administered to different groups of participants is referred to as: Select one: A.counterbalancing B.changing criterion C.Latin square D.Solomon four-group

Correct Answer is: A Counterbalancing is a within-subjects design that involves changing the order in which each treatment is administered to different groups of participants. The goal is to use every possible treatment sequence with equal numbers of participants for each sequence. changing criterion The changing criterion design is a single case design consisting of a series of phases with differing behavioral criterion set for each. The treatment is considered effective if the behavior reaches the criterion level for each phase. Latin square If the number of participants is too small to permit the use of a completely counterbalanced research design, then researchers may use a type of partial counterbalancing like the Latin square design. This design is useful for determining what exact sequences of treatment will be administered to the different participant groups. Solomon four-group The Solomon four-group design is used to evaluate the effects of pretesting on internal and external validity.

A psychological researcher would like to determine what variables best distinguish between patients who benefit from psychotherapy and patients who do not. To identify these variables, the research would most likely use which of the following? Select one: A.discriminant function analysis B.factor analysis C.canonical correlation D.MANOVA

Correct Answer is: A Discriminant function analysis is used to identify variables that distinguish between two or more existing or naturally occurring groups. Its use would involve collecting data on a variety of measures and determining which combination of them best predict differences between the groups. Since the researcher's purpose is to find variables that distinguish between existing groups, discriminant function analysis is the best answer. Regarding the other choices, factor analysis is used to reduce variability in a set of variables to a smaller set of unobserved variables, or factors. For example, factor analysis might be use to confirm a theory that score differences on a variety of intelligence measures can be explained in terms of two factors, verbal intelligence and performance intelligence. Canonical correlation is a technique for assessing the relationship between two sets of variables: i.e., it is used to assess the relationship between multiple predictor and multiple criterion variables. And MANOVA, or multivariate analysis of variance, is used in research studies to evaluate the effects of one or more independent variables on multiple (two or more) dependent variables.

The best control for practice effects in an experiment is: Select one: A.counterbalancing. B.random selection. C.double-blind. D.equivalent groups.

Correct Answer is: A If subjects are getting a series of treatments, and the order of the presentation might affect the outcome, you would present their treatments in different orders in a counter-balanced design in order to control for the possible practice effect of receiving a set order of treatments.

A psychologist in a hospital is conducting research designed to assess the effects of a new drug on the social behavior of psychotic patients. Which of the following would be the best way to decrease experimenter bias in this type of study? Select one: A.a double-blind study B.counterbalancing C.a randomized block design D.a Solomon four-group design

Correct Answer is: A In a double-blind study, neither the experimenter nor the subjects know the research hypothesis. This technique thus controls for all types of experimenter and subject expectancies, including experimenter bias. The experimenter's bias in favor of the research hypothesis cannot influence the results of the study if he or she does not know the hypothesis.

Excessive variability in a behavior over time can make it difficult to obtain accurate information about the effects of an intervention on that behavior. Such variability poses the biggest threat for which of the following research designs? A.single-subject B.factorial C.split-plot D.Solomon four-group

Correct Answer is: A In a single-subject research design, the target behavior is measured at regular intervals throughout the baseline and treatment phases. If the behavior changes often in strength, intensity, or frequency, it would be difficult to obtain a clear baseline reading or to determine if the intervention is having the desired effect.

A moderator is Select one: A.a variable that affects the direction or strength of the association between two other variables. B.an explanation of how external physical events take on internal psychological significance. C.a variable that identifies the relationship between two variables and serves to magnify the strength of the variables. D.a variable that accounts for the relationship between two variables.

Correct Answer is: A In general, a moderator is a qualitative (e.g., race, sex, class) or quantitative (e.g., level of reward) variable that affects the direction and/or strength of the relation between an independent or predictor variable and a dependent or criterion variable. A moderator only influences the strength of the relationship between two other variables, it doesn't fully account for it. In contrast, a variable functions as a mediator to the extent that it accounts for the relation between the predictor and the criterion.

Randomly selecting certain schools from a large school district and then including all teachers in those schools or a random sample of teachers from those schools in your study is referred to as Select one: A.cluster sampling B.stratified sampling. C.systematic sampling. D.nested sampling.

Correct Answer is: A In this situation, you are starting out the sampling processing by selecting naturally occurring groups (clusters) of subjects. This is referred to as cluster sampling, and is useful when it's not feasible to directly sample individuals from the population.

Using an 8 hour comprehensive battery of tests to assess intelligence, subjects' performance systematically declined in the later hours of assessment. This is due to which of the following threats to internal validity? A.maturation B.testing C.selection D.instrumentation

Correct Answer is: A Maturation refers to any internal change (biological or psychological) that occurs in subjects while an experiment is in progress and which results in a systematic effect on the DV. Such a long duration of testing would probably cause subjects to be fatigued, which would affect their performance on a test of intelligence. Testing refers to subjects' improved performance on a post-test due to their experience with the pre-test. Selection refers to pre-existing subject factors that account for the results in the DV (for example, in measuring the effects of an exercise program, the participants chosen for the program were more athletic than the control group). Instrumentation threatens internal validity when the measuring process changes between the pre- and post-tests (e.g.: raters become better at rating with practice).

A test measuring verbal fluency is administered to 250 college students, and a split-half reliability coefficient is obtained. If the same test instead had been administered to 250 students aged 12-21, the obtained reliability coefficient probably would have Select one: A.been higher. B.been lower. C.remained about the same. D.moved from negative to positive.

Correct Answer is: A One factor that affects any correlation coefficient, including a reliability coefficient, is the range of scores. If the range of scores is restricted on either or both sets of scores, the correlation coefficient will be lowered. The two sets of scores involved in a split-half reliability coefficient are scores obtained by the same group of individuals on two different halves on the test. Originally, the test was administered to only college students. In the second scenario, the test was administered to a broader range of students.

In principal components analysis, an eigenvalue would indicate: A.the amount of variability in a group of variables accounted for by an independent statistical component. B.the amount of variability in a group of variables accounted for by a statistical component that shares variability with other statistical components in the analysis. C.the amount of variability in one measured variable accounted for by all the independent statistical components in the analysis. D.the amount of variability in all measured variables accounted for by all the statistical components in the analysis.

Correct Answer is: A Principal components analysis and factor analysis are two complex statistical techniques designed to determine the degree to which a large set of variables can be accounted for by fewer, underlying constructs (referred to as "factors" or "principal components"). In principal components analysis and factor analysis, an eigenvalue is a statistic that indicates the degree to which a particular factor is accounting for variability in the variables studied. In other words, a factor's eigenvalue indicates its strength or explanatory power.

Which of the following techniques would not be useful for controlling or assessing the effects of an extraneous variable? A.stratified random sampling B.blocking C.matching D.ANCOVA

Correct Answer is: A Stratified random sampling involves dividing a population of interest into sub-populations (strata) and obtaining random samples from each strata. For instance, a researcher interested in studying the American population as a whole may break it down by ethnic groups and take proportionate random samples from each. The technique is designed to ensure that subjects are representative of the population of interest. Unlike the other choices, it is not used to control for the effects of an extraneous variable.

The technique which allows a researcher to identify the underlying (latent) factors that relate to a set of measured variables and the nature of the causal relationships between those factors is: Select one: A.structural equation modeling (SEM) B.cluster analysis C.Q-technique factor analysis D.survival analysis

Correct Answer is: A Structural equation modeling is a multivariate technique used to evaluate the causal (predictive) influences or test causal hypotheses about the relationships among a set of factors. Cluster analysis* is used to identify homogeneous subgroups in a heterogeneous collection of observations. Q-technique factor analysis* determines how many types of people a sample of people represents. Survival analysis* is used to assess the length of time to the occurrence of a critical event (* incorrect options).

Which of the following would increase the power of a statistical test? A.an increase in alpha B.a decrease in alpha C.a decrease in sample size D.use of a two-tailed test

Correct Answer is: A The "power" or sensitivity of a statistical test is the probability of rejecting the null hypothesis when it is false, that is, the probability of correctly identifying that a difference exists. When alpha is increased (e.g., from .01 to .05), it becomes easier to reject the null hypothesis and, consequently, power is also increased. All of the other choices (a decrease in alpha, a decrease in sample size, or the use of a two-tailed test) would decrease the test's power.

Which of the following would increase the power of a statistical test? Select one: A.an increase in alpha B.a decrease in alpha C.a decrease in sample size D.use of a two-tailed test

Correct Answer is: A The "power" or sensitivity of a statistical test is the probability of rejecting the null hypothesis when it is false, that is, the probability of correctly identifying that a difference exists. When alpha is increased (e.g., from .01 to .05), it becomes easier to reject the null hypothesis and, consequently, power is also increased. All of the other choices (a decrease in alpha, a decrease in sample size, or the use of a two-tailed test) would decrease the test's power.

When a multiple regression analysis is employed to predict outcome, there should be Select one: A.low intercorrelations among the predictors and high correlation of each predictor with the criterion. B.high intercorrelations among the predictors and high correlation of each predictor with the criterion. C.low intercorrelations among the predictors and low correlation of each predictor with the criterion. D.high intercorrelations among the predictors and low correlation of each predictor with the criterion.

Correct Answer is: A This question has come up in other examples throughout the tests. Simply stated, we need to have a high correlation between the predictor and the criterion we're making predictions about (this eliminates two of the four alternatives). Also, we need to have the predictors themselves be more or less independent of each other. That is, they shouldn't intercorrelate. If they do, then there's no point in using all of them -- if they all measure the same thing, why not use just one? So, you don't want the predictors to intercorrelate.

In a normal distribution of scores, the range of raw scores represented by the percentile rank range of 50 to 55 is _______ the range of raw scores represented by the percentile rank range of 90 to 95. Select one: A.less than B.greater than C.the same as D.depending on the standard deviation, either less than, greater than, or the same as

Correct Answer is: A This question is a bit tricky and requires careful reading and a good grasp of the concepts of percentile rank and normal distribution. The easiest way to understand it is in terms of an example. Say that you have a test, with a mean of 70 and a range of possible raw scores from 0-100. The raw score mean is 70; in a normal distribution, the mean is equivalent to a percentile rank of 50 and is in the exact middle of the distribution (if you don't know why this is, go back and review the Statistics section before attempting to understand this question). In a normal distribution, most of the raw scores are near or at the middle of the distribution; thus, most of the raw scores will be near or at 70. Similarly, the PR score range of 50 to 55 is in the middle part of the distribution, which is to say that most of the raw scores in this part of the distribution will be at or near 70. So the raw score range set by PR 50 to PR 55 will not be wide. Now if you look at a normal curve, you will see that in the high end of the raw score distribution, there is a long tail spread across the bottom. This reflects the fact that there are relatively few high scorers, and the scores of these individuals are spread out (over the length of this tail). Since the 90 to 95 PR range is in the high end of the distribution, the range of raw scores here will be relatively higher than the range of raw scores in the middle of the distribution. If you chose "the same as", you probably did so based on the fact that the percentile rank distribution is flat. This means that the same amount of people will score between 50 and 55 and 90 and 95. However, the question is not about how many people will score within this PR range. Instead, it's asking about the raw score ranges these PR ranges correspond to. If you didn't understand the above explanation, it might be useful to read it again with the normal curve in front of you. As you're looking at the curve, remember that it is a raw score distribution, and try to approximate where the percentile ranks in the question would be placed on this distribution. If you still don't understand, don't worry too much. As you review this concept and practice with more questions, these things will be come clearer and clearer. Remember that difficult technical content requires repeated review, so you should keep track of those particular concepts you need to review on a regular basis.

To use the statistical technique known as trend analysis, you need: Select one: A.a quantitative independent variable. B.a linear relationship between independent and dependent variables. C.a true experimental research design. D.two or more independent variables.

Correct Answer is: A Trend analysis is what is sounds like; i.e., it is used to identify trends and, therefore, requires a quantitative independent variable. You might use trend analysis, for example, to determine if amount of time you spend studying is related to your score on the licensing exam in a linear or nonlinear fashion.

An advantage of using a MANOVA over multiple one-way ANOVAs is that Select one: A.the use of a MANOVA reduces the experiment-wise error rate. B.a MANOVA can be used when the study involves more than one dependent variable. C.a MANOVA is the more appropriate test when the researcher has an a priori hypotheses about the nature of the relationship between the independent and dependent variables. D.a MANOVA involves simpler mathematical calculations.

Correct Answer is: A When a study involves two or more dependent variables, data can be analyzed with either multiple (one for each dependent variable) statistical tests (e.g., multiple one-way ANOVAs) or one MANOVA. An advantage of the latter technique is that it reduces the probability that at least one Type I error (incorrect rejection of the null hypothesis) will be made. This is because the fewer statistical tests one conducts, the less likely it is that a Type I error will occur. In an experiment that involves more than one comparison, the probability of at least one Type I error is referred to as the experiment-wise error rate.

A researcher has rejected the null hypothesis that group means are equal and has concluded that the mean of the experimental group is significantly higher than that of the control group. In reality, however, the population means of the two groups are equal. The researcher has made an error of which type? A.Type I B.Type II C.Experiment-wise D.Computational

Correct answer: A **When a series of significance tests is conducted, the experiment-wise error rate (EER) is the probability that one or more of the significance tests results in a Type I error.

Which of the following techniques would be appropriate when multiple predictors will be used to predict a score on a single criterion? Select one: A.multiple regression analysis B.multiple discriminant function analysis C.principal components analysis D.linear regression analysis

Correct Answer is: A When multiple predictors will be used to predict a score on a single criterion, multiple regression is appropriate. Discriminant function analysis is used to determine which continuous variables discriminate between two or more naturally occurring groups. Multiple discriminant function analysis, an extension of discriminant function analysis, involves using multiple predictors to sort individuals into one of three or more criterion groups. Principle components analysis, similar to factor analysis, is used to determine the variables or components that account for the total variance in test scores. When a single predictor is used to predict or estimate a score on a single criterion, linear regression is appropriate.

If you want to measure whether a weight training program resulted in significant changes in weight and strength for a sample of body builders, the best test to use is: Select one: A.MANOVA. B.paired t-tests. C.repeated measures ANOVA. D.chi-square.

Correct Answer is: A When you have two dependent variables (weight and strength), you would need a test that can handle two DVs. Among the four choices here, only the MANOVA can do that. Some people get stuck because they think the MANOVA requires more than one IV, but that's not the case. The requirement is that there is at least one IV and more than one DV.

To determine the relationship between a dichotomous variable and a continuous variable, you would use which of the following correlation coefficients? Select one: A.point biserial B.biserial C.Spearman's Rho D.eta

Correct Answer is: A You should memorize the different correlation coefficients and when they are used. The point-biserial coefficient is used when a dichotomous variable (e.g., gender) is correlated with continuous variable (e.g., IQ score). You might have thought the biserial coefficient is also correct, since it is used to correlate an artificial dichotomy with a continuous variable. An artificial dichotomy is one that is created arbitrarily by setting a cutoff score on a test; for instance, if you give the WAIS-IV and classify everybody who scores over 110 as having "high intelligence" and everybody who scores below 110 as having "low intelligence," you have created an artificial dichotomy. Because an artificial dichotomy is not, in a pure sense, a dichotomous variable, "biserial" is not as good an answer as "point biserial".

A psychologist wants to study the effectiveness of a new treatment she developed to reduce self-mutilative behaviors in patients with Borderline Personality Disorder. She plans to use a single-subject design but, if effective, she does not want to withdraw the treatment due to the potential harm that could result. She should, therefore, use which of the following research designs: Select one: A.ABAB B.multiple baseline C.reversal D.latin square

Correct Answer is: B A multiple baseline design is a single-subject design in which an independent variable is sequentially administered across two or more subjects, behaviors, or settings (i.e., across "baselines"). The multiple baseline design has the advantage of not having to withdraw the treatment once it has been applied to a baseline. Reversal designs, on the other hand, such as the ABA or ABAB designs have a second baseline (the second "A"), during which the treatment is withdrawn. The latin square design is not a single-subject design. Rather, it uses many subjects who are all administered all levels of an independent variable, but the order of administration varies between subjects or subgroups of subjects.

In designing a research study, you take a number of steps that have the effect of reducing beta. This means that you have reduced the probability of: Select one: A.retaining a true null hypothesis. B.retaining a false null hypothesis. C.rejecting a true null hypothesis. D.rejecting a false null hypothesis.

Correct Answer is: B Beta is the probability of making a Type II error, or of retaining a false null hypothesis. In plain language, it is the probability of failing to detect a true effect.

A researcher wants to obtain the correlation between several academic predictor tests and three measures of academic success in college. The appropriate method of correlational analysis to use would be the Select one: A.Pearson Product Moment Correlation Coefficient. B.canonical correlation. C.multiple correlation. D.factor analysis.

Correct Answer is: B Canonical correlation is a method used to assess the relationship between two sets of variables--i.e., two or more predictor variables and two or more criterion variables. Scores on both sets of variables are weighted and summed to come up with two canonical variates, or weighted sum scores, one for the predictor variables and one for the criterion variables, and the results reflect how strongly the two canonical variates are related. The Pearson Product Moment Correlation Coefficient is used to test the strength of the relationship between two variables only, one predictor and one criterion (e.g., one academic test and college GPA). When there are two or more predictor variables and one criterion variable, multiple correlation can be used. And factor analysis is not a correlational method, but instead a way of finding a few, unobserved variables that could account for scores on many variables. (factor analysis is a method to establish a test's construct validity)

A researcher inquires about the subjects' performance expectations and beliefs about the purpose of the study at the conclusion of the experiment. The researcher finds the subjects' actual performance is consistent with their beliefs and expectations when analyzing the data. The results of the study may be confounded by: Select one: A.the Hawthorne effect B.demand characteristics C.carryover effects D.changing criteria

Correct Answer is: B Demand characteristics are unintentional cues in the experimental environment or manipulation that affect or account for the results of the study. In this situation, the subjects' may have acted in ways consistent with their expectations rather than simply in response to the experimental manipulation. The Hawthorne effect occurs when research subjects act differently because of the novelty of the situation and the special attention they receive as research participants. Carryover effects occur in repeated measures designs when the effects of one treatment have an impact on the effects of subsequent treatments.

A criterion-related validation study of a cognitive employment test (n = 1000) for a particular job shows a statistically significant validity coefficient of .40 with supervisory ratings of job performance. Regressing supervisory job performance rating on a race and test score reveals a statistically significant interaction between race and score. If no further information is available, the validation findings are best characterized as: A.consequential validity. B.single group validity. C.adverse impact. D.differential prediction.

Correct answer: D

A researcher would use which of the following techniques to classify people into criterion groups based on their scores or status on two or more predictors? Select one: A.structural equation modeling B.discriminant function analysis C.cluster analysis D.multitrait-multimethod matrix

Correct Answer is: B Discriminant function analysis is used to determine which variables discriminate between two or more naturally occurring groups. Discriminant function analysis is multivariate analysis of variance (MANOVA) reversed. In MANOVA, the independent variables are the groups and the dependent variables are the predictors. In DA, the independent variables are the predictors and the dependent variables are the groups. As previously mentioned, DA is usually used to predict membership in naturally occurring groups. It answers the question: can a combination of variables be used to predict group membership? Usually, several variables are included in a study to see which ones contribute to the discrimination between groups. ---> use discriminant function analysis when goal is to use two or more predictors to predict or estimate status on one crtierion measured on a nominal scale Structural equation modeling is used to evaluate the cause-and-effect or predictive relationships between measured variables and latent factors. The multitrait-multimethod matrix is used to evaluate convergent and divergent validity. Cluster analysis is a method for grouping objects of similar kind into respective categories. It can be used to discover structures in data without providing an explanation/interpretation.

Choosing a correlation coefficient is based on factors such as the variables scale of measurement and the shape of the relationship between them. When measuring the relationship between two continuous variables when the relationship between them is nonlinear, which of the following is used? Select one: A.rho B.eta C.phi D.tau

Correct Answer is: B Eta is the appropriate correlation coefficient to use when both variables are measured on an interval or ratio scale and the relationship between the predictor (the X variable) and the criterion (the Y variable) is curvilinear. Rho (sometimes referred to as the Spearman rank-order correlation coefficient) is appropriate when both variables are measured as ranks. The phi coefficient is used when both variables are true (natural) dichotomies. When both variables are measured on an ordinal scale, Kendall's tau is appropriate.

Which one of the following is least likely to attenuate a measure of correlation? Select one: A.restricted range B.homoscedasticity C.curvilinear relationship D.the use of unreliable measures

Correct Answer is: B Homoscedasticity refers to even scatter around the regression line. Homoscedasticity is actually a good thing. It wouldn't attenuate the correlation at all. The other three choices list factors that would attenuate the correlation coefficient. (attenuation = the reduction of force/strength)

You are investigating whether there is a relationship between the number of years one has been smoking cigarettes and the number of psychotherapy sessions required to quit smoking. The best statistical method to analyze the results is: Select one: A.chi-square B.Pearson r C.t-test for independent samples D.multiple regression analysis

Correct Answer is: B In this case, you are attempting to assess the relationship between two variables that are measured on a continuous (interval or ratio) scale. The Pearson r allows you to do this. The Pearson r is the bivariate (i.e., for two variables) correlation coefficient used when variables are measured on an interval or ratio scale.

Of the following, which is designed most explicitly to assist an investigator in deciding how much confidence to put in a particular finding based on data? Select one: A.descriptive statistics B.inferential statistics C.measures of central tendency D.correlation coefficient

Correct Answer is: B Inferential statistics are used to make inferences from data in a study to more general conditions. For example, determining the probability that an observed difference between groups is dependable or happened by chance. Most of the major inferential statistics come from a general family of statistical models known as the General Linear Model which includes the t-test, Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA), regression analysis, and many of the multivariate methods like factor analysis, multidimensional scaling, cluster analysis, and discriminant function analysis. descriptive statistics Descriptive statistics simply to describe what the data is and/or what it shows in a study. measures of central tendency Central tendency is the center or middle of a distribution and three most common measures of central tendency are the mean, the median, and the mode. correlation coefficient Correlation coefficient is a statistical measure of the interdependence of two or more random variables wherein the value indicates how much of a change in one variable is explained by a change in another.

LISREL would be most useful in Select one: A.testing the relationship between multiple predictor variables and one criterion variable. B.confirming hypotheses regarding relationships between several latent and observed variables. C.testing a hypothesis regarding the effects of multiple independent variables on one dependent variable. D.validating questions on a personality inventory.

Correct Answer is: B LISREL is an acronym that stands for linear structural relations analysis. It is a software program used in Structural Equation Modeling, which is a technique used to test theories regarding unidirectional and bi-directional relationships among latent (unobserved) and manifest (observed) variables. Most experts recommend that Structural Equation Modeling should be used as a confirmatory method, to provide evidence for a theory as opposed to exploratory method to originate new theories.

When processing data of "low quality," from small samples, or on variables about which nothing is known concerning their distribution, which statistical procedure would be most appropriate? Select one: A.parametric B.non-parametric C.path analysis D.discriminant function analysis

Correct Answer is: B Nonparametric methods were developed to be used in cases when the researcher knows nothing about the parameters of the variable of interest in the population (hence the name nonparametric). In more technical terms, nonparametric methods do not rely on the estimation of parameters (such as the mean or the standard deviation) describing the distribution of the variable of interest in the population. Therefore, these methods are also sometimes (and more appropriately) called parameter-free methods or distribution-free methods. nonparametric tests = Inferential statistical tests designed to be used with nominally-and ordinally-scaled data; they do not make the same assumptions (normal distribution, homogeneity of variance) as parametric tests and are therefore less powerful.

Mary obtains a percentile rank of 93 on a chemistry test and John obtains a 58 percentile rank on the same test. Due to errors in scoring their exams, 5 points have to be added to each of their raw scores, but not to the scores of the other examinees. This should cause: A.Mary's percentile rank to increase more than John's B.John's percentile rank to increase more than Mary's C.the percentile ranks to increase by the same amount D.the percentile ranks to remain the same

Correct Answer is: B Percentile ranks are evenly distributed, creating a flat or rectangular distribution of percentile scores. However, raw scores in a normal distribution, form a bell-shape, with most of the scores clustered in the center of the distribution and fewer scores at the high and low ends. Therefore, when points are added to a raw score at the 93rd percentile rank (located at the high end of the distribution), it will "jump over" fewer scores than when the same number of points are added to a raw score at the 58th percentile rank (located closer to the center of the distribution).

A condition necessary for pooled variance is: A.unequal sample sizes B.equal sample sizes C.unequal covariances D.equal covariances

Correct Answer is: B Pooled variance is the weighted average variance for each group. They are "weighted" based on the number of subjects in each group. Use of a pooled variance assumes that the population variances are approximately the same, even though the sample variances differ. When the population variances were known to be equal or could be assumed to be equal, they might be labeled equal variances assumed, common variance or pooled variance. Equal variances not assumed or separate variances is appropriate for normally distributed individual values when the population variances are known to be unequal or cannot be assumed to be equal.

If a behavior reaches the criterion level for each phase, treatment is considered to be effective in which of the following designs? Select one: A.multiple baseline B.changing criterion C.Solomon four-group D.Latin square

Correct Answer is: B The changing criterion design is a type of single case design that consists of a series of phases in which a different behavioral criterion is set for each phase. If the behavior reaches the criterion level for each phase, the treatment is considered to be effective. The multiple baseline* sequentially applies a treatment across subjects, settings, or behaviors in a single case design. The Solomon four-group design* is utilized to assess the pretesting effects on the internal and external validity of a study. Partial counterbalancing is used when the number of participants does not permit a completely counterbalanced design. Latin square* design, a type of partial counterbalancing, administers all levels of an independent variable to all subjects but the order of administration varies between subjects or subgroups of subjects (* incorrect options).

In computing test reliability, to control for practice effects one would use a(n):I. split-half reliability coefficient.II. alternative forms reliability coefficient.III. test-retest reliability coefficient. Select one: A.I and III only B.I and II only C.II and III only D.II only

Correct Answer is: B The clue here is the practice effect. That means that if you give a test, just taking it will give the person practice so that next time, he or she is not a naive person. To control for that, we want to eliminate the situation where the person is administered the same test again. So we do not use test-retest. We can use the two other methods listed. We can use split-half since, here, only one administration is used (the two parts are thought of as two different tests). And, in the alternative forms method, a different test is given the second time, controlling for the effects of taking the same test twice.

Which of the following correlation coefficients is used to assess convergent validity: Select one: A.heterotrait-monomethod B.monotrait-heteromethod C.heterotrait-heteromethod D.monotrait-monomethod

Correct Answer is: B The response choices make up a multitrait-multimethod matrix, a complicated method for assessing convergent and discriminant validity. Convergent validity requires that different ways of measuring the same trait yield the same result. Monotrait-heteromethod coefficients are correlations between two measures that assess the same trait using different methods; therefore if a test has convergent validity, this correlation should be high. Heterotrait-monomethod and heterotrait-heteromethod both confirm discriminatory validity, and monotrait-monomethod coefficients are reliability coefficients.

A 10-year-old child is administered the WISC-V and obtains a score of 140 Full-Scale IQ. If she is retested at the age of 16, her IQ score will most likely be: A.higher B.lower C.the same D.impossible to predict

Correct Answer is: B This question was a little tricky in that it appears to be about the reliability of IQ scores over time when it is really a statistics question. The WISC-V does have very good reliability over time and, if the IQ score was in the normal range, we could predict that it would stay the same over time. However, a score of 140 on the WISC-V is extremely high (classified as "very superior") and would, therefore, likely be lower upon retesting due to regression to the mean -- which is the tendency of extreme scores to be less extreme upon retesting.

If you are interested in determining whether the relationship between arousal and performance assumes a linear or a non-linear shape, the best statistical analysis to use would be A.multiple regression analysis. B.trend analysis. C.logistic regression. D.principal components analysis.

Correct Answer is: B Trend analysis is a statistical technique used to determine the trend or shape that best describes the relationship between two variables. The technique basically involves collecting data on two variables and running statistical analyses to determine what trend or trends (e.g., linear, U-shaped) are significant. For example, in studying the relationship between arousal and performance, one could study 100 students and collect data on how aroused they are and how well they perform. Then, one could run a separate analysis for different types of trends and see which receives the strongest support.

If you are interested in determining whether the relationship between arousal and performance assumes a linear or a non-linear shape, the best statistical analysis to use would be Select one: A.multiple regression analysis. B.trend analysis. C.logistic regression. D.principal components analysis.

Correct Answer is: B Trend analysis is a statistical technique used to determine the trend or shape that best describes the relationship between two variables. The technique basically involves collecting data on two variables and running statistical analyses to determine what trend or trends (e.g., linear, U-shaped) are significant. For example, in studying the relationship between arousal and performance, one could study 100 students and collect data on how aroused they are and how well they perform. Then, one could run a separate analysis for different types of trends and see which receives the strongest support.

In order to compare the results from two or more different studies that measure the same concept but used different outcome measures, you would be most interested in the: A.p-value B.effect size C.order effects D.credentials of the researcher, since it's impossible to compare data from different studies.

Correct Answer is: B When comparing data from different studies which measure the same concepts, you would be conducting a meta-analysis which results in an effect size. An effect size indicates the average effect of a treatment across many different studies. The p-value, also known as the alpha level, indicates the probability that the null hypothesis is false, which, as always, is useful to know, but is not as useful as the effect size in making comparisons between studies. Order (or carryover) effects are the effects of the order of treatment administration in a repeated measures design.

A study comparing the effectiveness of stress inoculation training, hypnosis, and EMDR on PTSD measures a client's anxiety level prior to receiving one treatment and at 6 and 12 weeks after beginning treatment. This design would be considered: A.between-groups B.within-subjects C.mixed D.counterbalanced

Correct Answer is: C A mixed research design has at least one between-subjects independent variable and at least one repeated measures variable (or within-subjects variable). Since this study is comparing the effects on three different groups of subjects (i.e. a between-subjects design) combined with the use of a repeated measures (within-subjects) design, it would be considered a mixed design. Counterbalancing is a technique used to control order effects in a repeated measures design, and involves administering the treatments to the different groups in a different order.

Likert scales are most useful for: Select one: A.dichotomizing quantitative data B.quantifying objective data C.quantifying subjective data D.ordering categorical data

Correct Answer is: C Attitudes are subject phenomena. Likert scales indicate the degree to which a person agrees or disagrees with an attitudinal statement. Using a Likert scale, attitudes are quantified - or represented in terms of ordinal scores.

A meta-analysis differs from a literature review in that Select one: A.a meta-analysis involves a review of research in a more broadly defined area than a literature review. B.a literature review involves a review of research in which no statistical hypothesis tests were used. C.a meta-analysis, but not a literature review, involves calculation of an effect size. D.a literature review, but not a meta-analysis, involves calculation of an effect size.

Correct Answer is: C Both meta-analyses and literature reviews involve a summation of research studies related to a particular topic or hypothesis. However, unlike a literature review, a meta-analysis involves calculation of an "effect size," or a statistic that indicates the average effect of a particular treatment across the studies reviewed. This involves converting data from many different studies into a common metric so that their results can be combined and compared. A literature review, by contrast, provides descriptive information only.

An examinee obtains a score of 70 on a test that has a mean of 80, a standard deviation of 15, and a standard error of measurement of 5. The 95% confidence interval for the examinee's score is: A.50-90 B.55-85 C.60-80 D.65-75

Correct Answer is: C Confidence interval indicates the range within which an examinees' true score is likely to fall, given his or her obtained score. The standard error of measurement indicates how much error an individual test score can be expected to have and is used to construct confidence intervals. To calculate the 68% confidence interval, add and subtract one standard error of measurement to the obtained score. To calculate the 95% confidence interval, add and subtract two standard errors of measurement to the obtained score. Two standard errors of measurement in this case equal 10. We're told that the examinee's obtained score is 70. 70 + 10 results in a confidence interval of 80 to 100. In other words, we can be 95% confident that the examinee's true score falls within 60 and 80.

When trying to prove causation, a researcher mismatches levels of data and tries to apply statistics at one level to infer to data of another level. This is referred to as: Select one: A.tautology B.teleology C.ecological fallacy D.latent coding

Correct Answer is: C Ecological fallacy is a logic error that occurs when trying to prove causation, levels of data are mismatched and statistics are applied at one level to infer to data of another level. Tautology* is a logic error based on circular reasoning, meaning that something is true by definition or the dependent variable is simply a restatement of the independent variable. Teleology* is a logic error which explains a phenomenon by saying that it was some spirit or higher power that causes the relationship. Latent coding* occurs when a researcher reads into the meaning of the content he/she is analyzing to get data rather than simply taking it at face value. This is in contrast to manifest coding which occurs in content analysis when coding content is based on the face-value rather than looking into the meaning (* incorrect options).

Increasing internal validity is best achieved by: Select one: A.random selection B.matching C.random assignment D.blocking

Correct Answer is: C Known as the "great equalizer," randomization of subjects to groups is the most powerful way for controlling extraneous variables. Unlike random assignment which occurs after subjects are selected, random selection refers to a method of selecting subjects to participate from the population being studied. Random selection influences external validity. Matching, a procedure to ensure equivalency on a specific extraneous variable, and blocking, studying the effects of the extraneous variable, are also methods of increasing internal validity.

You conduct a study designed to assess the effectiveness of psychotherapy in the treatment of depression. You work with two groups, one of which receives the therapy and one of which is an attention-only control group. All of your subjects are hospitalized inpatients; thus, all of them are extremely depressed and therefore score extremely low on your pretest measure of depression. The biggest threat to external validity in this study is: Select one: A.regression to the mean B.reactivity C.interaction between selection and treatment D.pretest sensitization

Correct Answer is: C Note that you are being asked for the biggest threat to external validity, not internal validity in this question. Therefore, you can rule out regression to the mean, which is generally viewed as a threat to internal validity (regression probably wouldn't threaten internal validity anyway in this case, since both groups appear to be equivalent in terms of their baseline depression levels).External validity refers to the generalizability of research results. An "interaction between selection and treatment" means that the effect of a treatment may not generalize to other members of the target population who differ in some way from the research subjects. For example, in this case, it's possible that your therapy is effective for individuals who are highly depressed, but would not have any effect on individuals who are moderately depressed.

One advantage of standard scores as compared to percentile ranks is that standard scores Select one: A.allow you to determine the relative standing of examinees who take the same test. B.set cutoff scores above which a given percentage of examinees will score. C.provide more meaningful information about differences between examinees' test scores. D.when used properly, can decrease the cultural bias of test scores in many cases.

Correct Answer is: C One disadvantage of percentile ranks is that a given distance between two percentile ranks does not necessarily reflect the same distance between the examinees' raw scores. Specifically, percentile ranks tend to overestimate raw score differences in the middle of the score distribution and underestimate raw score differences at the end of the distribution. Let's take an example: Say that Examinee A has a percentile rank score of 93, Examinee B has a percentile rank score of 96, Examinee C has a percentile rank score of 50, and Examinee D has a percentile rank score of 53. If you're just looking at percentile ranks, you might assume that the score difference between Examinee A and B is equivalent to the score difference between Examinee C and D. However, because examinees A and B scored at the extreme high end of the distribution, their raw score difference will be greater than that between examinees C and D, who scored in the middle of the distribution.

Path analysis is useful for: A.examining the unidirectional relationships among a set of measured and latent traits. B.examining the bidirectional relationships among a set of measured and latent traits. C.examining the unidirectional causal relationships among a set of measured traits. D.examining the bidirectional causal relationships among a set of measured traits.

Correct Answer is: C Path analysis is a causal modeling technique. It is somewhat limited compared to other techniques because it permits only one-way (unidirectional) paths between variables and involves looking only at the relationships among measured variables. (LISREL, a more complicated technique, looks at both measured variables and the latent traits measured by those variables and permits one- and two-way paths.)

Counterbalancing is a within-subjects design that entails changing the order each treatment is administered to different groups of subjects with the goal being to use each sequence of treatments with an equal number of participants for each. Researchers would use which of the following if the number of subjects in a study is too small to use a completely counterbalanced research design? Select one: A.changing criterion B.Solomon four-group C.Latin square D.multiple baseline

Correct Answer is: C Researchers may use partial counterbalancing, like the Latin square design, when the number of subjects doesn't allow a completely counterbalanced design. The Latin square design helps establish the specific sequences of treatment to be administered to different groups of subjects. The changing criterion is a type of single case design involving a series of phases in which a differing behavioral criterion is set for each. The treatment is deemed to be effective if the behavior reaches the criterion level for each phase. The Solomon four-group design is used to determine the effects of pretesting on internal and external validity. The multiple baseline is a type of single case design involving sequentially administering a treatment across subjects, behaviors or settings.

Which of the following is a measure of "amount of variability accounted for" Select one: A.alpha B.Cohen's d C.eta squared D.F-ratio

Correct Answer is: C The "amount of variability accounted for" is assessed by a squared correlation coefficient. Eta squared is the square of the correlation coefficient (i.e., the correlation between the treatment and the outcome) and is used as an index of effect size. Alpha* is the level of significance set by a researcher prior to analyzing the data. Cohen's d* is used as an index of effect size, but it is a measure of the mean difference between two groups. The F-ratio* is the statistic calculated when using the analysis of variance (* incorrect options).

__________________________ is used to statistically remove the effects of an extraneous variable on the dependent variable making it easier to detect the effects of the independent variable on the dependent variable. Select one: A.Factorial ANOVA B.Split-plot (mixed) ANOVA C.ANCOVA D.MANOVA

Correct Answer is: C The analysis of covariance (ANCOVA) is used to adjust dependent variable scores to control for the effectiveness of the covariate, or an extraneous variable, making it easier to determine the effects of the independent variable on the dependent variable. Factorial ANOVA A factorial ANOVA is used to analyze data when a factorial design, which includes two or more independent variables, is used and the dependent variable is measured on an interval or ratio scale. Split-plot (mixed) ANOVA The split-plot (mixed) ANOVA is the appropriate technique when at least one independent variable is a between-groups variable and another independent variable is a within-subjects variable. MANOVA The multivariate analysis of variance (MANOVA) is a type of ANOVA used when the study has two or more dependent variables and at least one independent variable. A researcher could use the MANOVA when all the dependent variables are measured on a ratio or interval scale rather than using separate ANOVAs to evaluate the effects of each of the dependent variables, thus also helping control the experiment-wise error rate.

A colleague of yours is interested in studying the effects of aging on IQ scores. He consults with you for some ideas regarding how to proceed with this research. Which of the following types of research designs would you recommend? Select one: A.longitudinal B.cross-sectional C.cross-sequential D.multiple baseline

Correct Answer is: C The colleague is interested in conducting developmental research, in which the effects of development (e.g., aging) on a dependent variable (in this case, IQ scores) are investigated. Longitudinal, cross-sectional, and cross-sequential are all types of developmental research designs. Of these, cross-sequential research is the strongest from a scientific point of view. Cross-sequential research is a combination of cross-sectional and longitudinal research. In cross-sequential research, as in cross-sectional research, subjects are divided into age groups (e.g., young, middle-aged, and old). And, as in longitudinal research, subjects are assessed repeatedly on the dependent variable over time. Because cross-sequential research combines the methodology of the two strategies, it is not associated with the limitations of one or the other.

When a study has two or more independent variables the research design used is: Select one: A.MANOVA B.ANOVA-one way C.Factorial ANOVA D.ANCOVA

Correct Answer is: C The factorial ANOVA is used when a study involves more than one independent variable. A one way ANOVA is used when a study has one independent variable and more than two independent groups. MANOVA is used when the study has two or more dependent variables and ANCOVA is used to adjust dependent variable scores to control for the effectiveness of an extraneous variable.

An organization is interested in improving employee morale in an off-site office with 150 employees. An organizational psychologist is contracted to identify and train the employees with the lowest morale. The employees scoring in the bottom 10% of a pretest are selected for extensive training. At the conclusion of the training, a posttest is administered and improvement in scores is noted. Test performance improvement would be expected even without training because: Select one: A.There has been a lapse of time between the first and second administrations. B.Such tests are notably unreliable, particularly when based on small samples. C.Regression of scores toward the mean is to be expected as a purely chance phenomenon. D.The range for which the test was designed has been restricted by the method of sampling.

Correct Answer is: C The net effect of regression toward the mean is that the lower scores (or measurements) on the pretest tend to be higher on the posttest, and the higher scores (or measures) on the pretest tend to be lower on the posttest. It is important to note that regression is always to the population mean of a group. However, there is essentially no change from the pretest to the posttest due to the dependent variable or treatment. It is important to note when conducting experiments because it affects the internal validity of the experimental design and occurs whenever the sample or subjects are chosen on the basis of extreme pretest scores. Regression to the mean refers to the tendency of extreme observations to be less extreme upon re-testing or re-observation. Francis Galton applied this concept to heredity. He concluded that due to regression to the mean, individual variation in the species is limited (the opposite of choice A). That is, since extreme individuals will likely have less extreme offspring (e.g., short fathers are likely to have taller sons), the characteristics of a species can only vary within a limited range.

A psychologist is conducting research to evaluate the effectiveness of three predictor tests of overall mental health he has developed. He administers the predictors to 35 individuals randomly chosen from the population of interest and obtains a squared multiple correlation coefficient (R2) of .47. If the psychologist administers the predictors to another 70 individuals drawn from the same population, the best prediction is that, he would obtain an R2 that is: Select one: A.lower than .47. B.about equal to .47. C.slightly to moderately higher than .47. D.much higher than .47.

Correct Answer is: C The principle behind this question is that the greater the range of scores in both the predictor(s) and the criterion, the higher the validity coefficient will be. If you administer the predictors to 70 people as opposed to 35, you are likely to get a somewhat greater range of scores in the former case. Therefore, you will get a somewhat higher correlation coefficient. This choice ("much higher than .47") is not a good answer. Increasing the range of scores can only do so much for your correlation coefficient, especially if you already have a reasonably representative sample to begin with. Increasing the sample size from 35 to 70, for example, will not turn a poor set of predictors into a good one. Some of you might have gone for this choice ("lower than .47"), thinking that, due to shrinkage, the correlation coefficient would be smaller. Shrinkage, however, is associated with the development of a predictor or set of predictors. It occurs when, based on research with one sample, items for a predictor are chosen from a larger pool, and the newly developed predictor is then tested on a second sample. The correlation coefficient for the second sample is likely to be smaller, because the predictor was "tailor made" for the first sample. In this question, however, the predictors are not in the process of development, and the first group of 35 people is not a validation sample (i.e., a sample of people used to determine which items to retain for the final version of the test). To see if you understand this distinction, try to rewrite the question, changing as few words as possible, so that this ("lower than .47") becomes the best answer.

While studying the use of journaling in the treatment of depression, a researcher finds only individuals with good writing ability benefit from journaling. Writing ability is a(n): Select one: A.outcome variable B.mediating variable C.moderator variable D.feedback variable

Correct Answer is: C The strength of the relationship between the independent and dependent variables is affected by a moderator variable. Writing ability is moderating the effects of journaling on the treatment of depression. Outcome variable* is another term for dependent variable. A mediating variable* is affected by the independent variable and affects the dependent. It is responsible for an observed relationship between an independent variable and a dependent (outcome) variable. A feedback variable* is an unrelated term (* incorrect options).

You are testing a cross-section of minority clients including New Zealanders, Hispanics, African-Americans and Asians. The New Zealander's group turns out to serve as a moderator variable. This means the test has: A.Cross validation B.Shrinkage C.Differential validity Correct D.Criterion contamination.

Correct Answer is: C Variables that affect the validity of a test are moderator variables. When a moderator variable is present a test is said to have differential validity--meaning there would be a different validity coefficient for the New Zealanders group than for the others.

Which of the following correlation coefficients indicates the strongest predictive relationship between two variables? A.0.76 B.0.09 C.-0.01 D.-0.84

Correct Answer is: D A correlation coefficient is a numerical value between -1 and +1 that expresses the strength and direction of the relationship between two variables. The negative or positive sign indicates the direction of the relationship, not its strength. So to determine which of these correlation coefficients indicates the strongest relationship, you have to consider the number alone and disregard the sign. Therefore, of the four choices, -0.84 represents the strongest relationship. To illustrate: let's say there is a strong negative relationship between income and unhappiness; hypothetically, we'll say that the correlation between yearly income and scores on the Beck Depression Inventory is -0.84. The "negative" part indicates the direction of the relationship and means that as one variable increases (income), the other (BDI scores) decreases. But the negative direction has nothing to do with the strength or weakness of the relationship; in this case, the relationship is negative and strong. Now consider a weak positive relationship, say between income and finger length. Hypothetically, let's say that the correlation is +0.10. A positive relationship means that the values of both variables tend to increase together. In this case, the relationship is very weak and any correlation between the two would be due to random chance factors alone. So in other words, positive or negative have nothing to do with the strength of a correlation; there can be strong negative correlations and weak positive ones (and vice versa of course).

rom a large population, you take two random samples and measure them on one dependent variable. In this case, you would expect the F-ratio to be close to: A.0.0. B.0.50. C.2.50. D.1.0.

Correct Answer is: D In statistical hypothesis testing, 1 is the expected value of the F ratio under the null hypothesis, or the hypothesis that two or more samples do not differ on some dependent variable measure. The null is rejected when F is significantly greater than 1. In other words, an F ratio of 1 suggests that the means being compared were drawn from the same population, which is the case here.Another way of looking at this question is to remember that MSB, the numerator of the F ratio, is a measure of between-group variability (differences between sample means), which is due to both treatment effects and error. But, if two random samples are taken from the same population, MSB reflects only error, since there is no treatment. And MSW, the denominator of the F ratio, is a measure of within-group variability, which reflects only error. So, in this case, the F ratio (MSB/MSW), instead of equaling "(treatment + error)"/error" will equal "error/error", or 1.

A study is conducted to determine the effectiveness of 3 different reading programs on reading comprehension. The participants are 5th grade students who are divided into 3 levels based on their past reading comprehension (below average, average, and above average). Results from a factorial ANOVA indicate that there are significant main effects of each variable and a significant interaction effect. Based on these results, one could conclude that: Select one: A.each of the reading programs is equally effective for students at every reading level B.only one of the reading programs is effective for students at every reading level C.the reading programs are only effective for students at a particular reading level D.the most effective reading program is dependent on the student's reading level

Correct Answer is: D A factorial ANOVA is used when a study has more than one independent variable. Factorial designs also allow for the assessment of both main effects (the effects of each independent variable considered individually) and interaction effects (the effects of each variable at the different levels of the other variable). The study described in this question has two "significant main effects" for the independent variables: type of reading program and past level of reading comprehension. And a "significant interaction effect" means that the effects of the different reading programs varied significantly for students at different reading levels. For example, "Reading Program A" may have been highly effective for above average students, moderately effective for average students, yet ineffective for below average students. On the other hand, "Reading Program B" may have been only effective for below average students, while "Reading Program C" may not have been effective for any students.

In a negatively skewed distribution, the order of measures of central tendency from lowest to highest will be Select one: A.mode, mean, median. B.mode, median, mean. C.mean, mode, median. D.mean, median, mode.

Correct Answer is: D A frequency distribution is a list of the values that a variable takes in a sample, showing the number of times each value appears. In a negatively skewed distribution, most of the values fall on the high end. For example, the distribution of scores on an easy test might be negatively skewed, with most people scoring high and only a few low scores. The distribution is called negatively skewed because when graphed on an x-y axis, the tapering side, representing fewer values, is on the left, or lower score, side. In a negatively skewed distribution, the mode is higher than the median, which is higher than the mean. The mode is the most frequently occurring score in the distribution, and on an easy test, the most frequent score will be toward the high end. And of the three measures of central tendency, the mean is most sensitive to outlying scores and will be pulled down by the few low scores. The median, the score that cuts the distribution in half, with 50% scoring above the median and 50% below, will fall in between the mean and the mode.

The Drugs-R-Us company wants to compare the effectiveness of 3 new antidepressant medications. Patients with depression are randomly assigned to one of the three medications and depressive symptoms are measured at weeks 1, 6, and 12. Which type of research design would be most appropriate for this study? A.ABAB B.between subjects C.within subjects D.mixed

Correct Answer is: D A mixed research design has at least one between-subjects independent variable and at least one repeated measures variable (or within-subjects variable). Since this study is comparing the effects on three different groups of subjects (i.e., a between-subjects variable) combined with the use of a repeated measures (within-subjects) variable, it would be considered a mixed design. An ABAB design is a type of reversal design, in which a baseline measure of a behavior is obtained (the "A" phase), the behavior is again measured after a treatment is administered (the "B" phase), the treatment is removed or reversed and the behavior is measured again (the second "A"), and the treatment is then re-applied (the second "B") and a final behavior measure is taken.

A test with limited ceiling would have a ____________ distribution shape. Select one: A.normal B.flat C.positively skewed D.negatively skewed

Correct Answer is: D A test with limited ceiling has an inadequate number of difficult items resulting in few low scores. Therefore the distribution would be negatively skewed.

In a study of 400 personality variables, it was found that 19 correlated at the .05 level of significance with a measure of actual behavior. The 19 significant correlations could be considered valid for: A.future research. B.future therapy. C.both future research and future therapy. D.neither future research nor future therapy.

Correct Answer is: D At the .05 level of significance, there is a 5% probability of making a Type I error. So, out of 400 relationships, you'd expect 20 or so to be found significant when they really aren't. Hence the 19 significant correlations probably aren't very meaningful. They likely have no application to either research or therapy.

A person obtains a raw score of 70 on a Math test with a mean of 50 and an SD of 10; a percentile rank of 84 on a History test; and a T-score of 65 on an English test. From highest to lowest, what is the relative order of each of these scores? Select one: A.History > Math > English B.Math > History > English C.History > English > Math D.Math > English > History

Correct Answer is: D Before we can compare different forms of scores, we must transform them into some form of standardized measure. A Math test which has a mean of 50 and an SD of 10 indicates that a raw score of 70 would fall 2 standard deviations above the mean. Assuming a normal distribution of scores, a percentile rank of 84 on a History test is equivalent to 1 standard deviation above the mean. If you haven't memorized that, you could still figure it out: Remember that 50% of all scores in a normal distribution fall below the mean and 50% fall above the mean. And 68% of scores fall within +/- 1 SD of the mean. If you divide 68% by 2 - you get 34% (the percentage of scores that fall between 0 and +1 SD). If you then add that 34% to the 50% that fall below the mean - you get a percentile rank of 84. Thus, the 84 percentile score is equivalent to 1 SD above the mean. Finally, looking at the T-score on the English test - we know that T-scores always have a mean of 50 and an SD of 10. Thus a T-score of 65 is equivalent to 1½ standard deviations above the mean. Comparing the 3 test scores we find the highest score was in Math at 2 SDs above the mean, followed by English at 1½ SDs above the mean, and History at 1 SD above the mean.

The use of "pooled variance" in statistics assumes that: Select one: A.the sample sizes are equal B.the sample variances are equal C.the population sizes are equal D.the population variances are equal

Correct Answer is: D Pooled variance is the weighted average variance for each group. They are "weighted" based on the number of subjects in each group. Use of a pooled variance assumes that the population variances are approximately the same, even though the sample variances differ.

Which of the following is NOT generally considered a direct threat to external validity? Select one: A.order effects B.hawthorne Effect C.interaction between selection and treatment D.history

Correct Answer is: D Distinguishing between internal and external threats to validity can be difficult. Indeed, some experts disagree on how to categorize some of them. However, all of the choices except "history" are generally considered to be threats to external validity. Order effects* (also known as carryover effects) occurs in repeated measures designs, or in studies in which the same subjects are exposed to more than one treatment. For example, in a study on the effects of marital therapy interventions, couples are given relaxation training followed by communication training. If significant improvement occurs, it may be due to relaxation training preceding communication training; therefore, the results could not be generalized to situations in which subjects only receive communication training. The Hawthorne effect* occurs when subjects behave differently due to the fact that they are participating in research. Obviously this threatens external validity since the results cannot be generalized to real-life situations in which people are not participating in research. Interaction between selection and treatment* refers to when a treatment has different effects depending on the selection of subjects. For example, studies that only use undergraduate students (as many studies do) might not generalize to non-undergraduate students (* incorrect options). Finally, history refers to an external event, other than the experimental treatment, that affects scores on the DV. This is primarily considered a threat to internal validity. For example, if a study on the effects of a new treatment for depression began several weeks before the events on "9-11" and concluded several weeks after "9-11," the results might indicate that the new treatment is not effective. However, this might not be a valid conclusion due to the effects of history.

A linear relationship is an assumption of all of the following, except: Select one: A.structural equation modeling B.regression analysis C.Pearson r D.eta

Correct Answer is: D Eta is a correlational coefficient used for non-linear, or curvilinear, relationships. Structural equation modeling*, a variety of techniques based on correlations between multiple variables, regression analysis*, a method used to estimate the value of one variable based on the value of another variable, and Pearson r*, all assume a linear relationship between variables (* incorrect options).

If a student scored between 1 and 2 standard deviations above the mean in a normal distribution of scores, you could conclude that the student's Select one: A.T-score is greater than 70 B.z-score is greater than 2 C.percentile rank is between 68 and 95 D.percentile rank is between 84 and 98

Correct Answer is: D If a score falls between 1 and 2 standard deviations in a normal distribution we can readily conclude that it's T-Score is between 60 and 70 and it's z-score is between 1 and 2 (since z-scores are stated in standard deviation units). We can, therefore, eliminate these two choices "T-score is greater than 70" and "z-score is greater than 2." To determine percentile ranks you can do a simple calculation if you know the areas under a normal curve. Remember that 50% of all scores in a normal distribution fall below the mean and 50% fall above the mean. And 68% of scores fall within +/- 1 SD of the mean. If you divide 68% by 2, you get 34% (the percentage of scores that fall between 0 and +1 SD). If you then add that 34% to the 50% that fall below the mean, you get a percentile rank of 84. Thus, the 84 percentile score is equivalent to 1 SD above the mean. The same calculation is used for determining the percentile rank at 2 standard deviations. Since 95% of all scores fall within +/- 2 SD, we divide 95% by 2 which equals 47.5 and add that to the 50% which falls below the mean, which totals 97.5 (rounded off = 98). Thus, the percentile rank is between 84 and 98.

Jose scored 75 on his final exam. The test scores were normally distributed, with a mean of 60 and a standard deviation of 15. Jose's score would be in which of the following percentile ranges? Select one: A.35 - 49 B.50 - 64 C.65 - 79 D.80 - 95

Correct Answer is: D In a normal distribution, 1.0 is 34 percentile points above the mean of 50. Jose's standard score is (75-60)/15 or 1.0, putting his score at the 84th percentile. In a normal distribution, approximately 68% of all scores fall between the scores that are -1.0 and +1.0 standard deviations from the mean, approximately 95% of all scores fall between the scores that are -2.0 and +2.0 standard deviations from the mean, and approximately 95% of all scores fall between the scores that are -3.0 and +3.0 standard deviations from the mean. For example, when a test has a mean of 100 and a standard deviation of 10 and test scores are normally distributed, you can conclude that about 68% of examinees obtained scores between 90 and 110, about 95% obtained scores between 80 and 120, and about 99% obtained scores between 70 and 130.

The sensitivity of a screening for a psychological disorder refers to Select one: A.the ratio of correct to incorrect diagnostic decisions its use results in. B.the proportion of correct diagnostic decisions its use results in. C.the proportion of individuals without the disorder it identifies. D.the proportion of individuals with the disorder it identifies.

Correct Answer is: D In any test used to make a "yes/no" decision (e.g., screening tests, medical tests such as pregnancy tests, and job selection tests in some cases), the term "sensitivity" refers to the proportion of correctly identified cases--i.e., the ratio of examinees whom the test correctly identifies as having the characteristic to the total number of examinees who actually possess the characteristic. You can also conceptualize sensitivity in terms of true positives and false negatives. A "positive" on a screening test means that the test identified the person as having the condition, while a "negative" is someone classified by the test as not having the condition. The term true and false in this context refer to the accuracy or correctness of test results. Therefore, sensitivity can be defined as the ratio of true positives (people with the condition whom the test correctly detects) to the sum of true positives and false negatives (all the examinees who have the condition).

From a large population, you take two random samples and measure them on one dependent variable. In this case, you would expect the F-ratio to be close to: Select one: A.0.0. B.0.50. C.2.50. D.1.0.

Correct Answer is: D In statistical hypothesis testing, 1 is the expected value of the F ratio under the null hypothesis, or the hypothesis that two or more samples do not differ on some dependent variable measure. The null is rejected when F is significantly greater than 1. In other words, an F ratio of 1 suggests that the means being compared were drawn from the same population, which is the case here.Another way of looking at this question is to remember that MSB, the numerator of the F ratio, is a measure of between-group variability (differences between sample means), which is due to both treatment effects and error. But, if two random samples are taken from the same population, MSB reflects only error, since there is no treatment. And MSW, the denominator of the F ratio, is a measure of within-group variability, which reflects only error. So, in this case, the F ratio (MSB/MSW), instead of equaling "(treatment + error)"/error" will equal "error/error", or 1.

In comparison with parametric statistical tests, nonparametric statistical tests: A.assume that observations are independent. B.require fewer assumptions about the data. require fewer assumptions about the data. C.possess enhanced predictive power. D.require larger sample sizes.

Correct Answer: B

Structural equation modeling is used to: Select one: A.classify participants into criterion groups based on their status or score on two or more predictors. B.to evaluate convergent and divergent validity. C.to identify homogeneous groups from a collection of observations. D.to evaluate predictive relationships between measured variables and latent factors.

Correct Answer is: D Structural equation modeling is a technique used to evaluate or confirm the cause-and-effect or hypothesized relationship between both measured and latent variables. classify participants into criterion groups based on their status or score on two or more predictors. Classifying participants into criterion groups based on their status or score on two or more predictors is referred to as discriminant function analysis. to evaluate convergent and divergent validity. Convergent and divergent validity is evaluated using the multitrait-multimethod matrix. to identify homogeneous groups from a collection of observations. Cluster analysis is a method for grouping objects of similar kind into respective categories. It can be used to discover structures in data without providing an explanation/interpretation.

The Solomon four-group design is: Select one: A.a quasi-experimental design B.used to analyze the difference scores among four different treatment groups C.used to reduce practice effects D.used to evaluate the effects of pretesting

Correct Answer is: D The Solomon four-group design is a true experimental design used to evaluate the effects of pretesting, since some groups are pretested and others are not.

You have conducted a study assessing the relationship between salary and job performance, and you find a significant correlation between these two variables. Your assistant tells you that the data fail to take into account a $25.00 cost of living raise which every employee received. You should: Select one: A.decide that the raise invalidated the research. B.reanalyze the data after the raises have been added to the current salary. C.not worry about small details; the actual amount is too small to make a significant difference. D.assume the correlation will not be affected.

Correct Answer is: D The basic point being tested here is that if you add a constant to each score -- in either or both data sets -- the relationship between the two variables won't be affected. In other words, adding a constant to every score does not affect the correlation coefficient. The same is true of multiplying or dividing all scores by a constant, or subtracting a constant from every score.

A score indicating the percentage of items correct on a test would be using which of the following scales of measurement? Select one: A.nominal B.ordinal C.interval D.ratio

Correct Answer is: D There are two defining characteristics of a ratio scale of measurement: 1) Successive data points on the scale reflect equally distant scores from each other. This is true of percentage scores; for instance, the difference between a score of 49% and 50% is the same as the difference between 50% and 51%. 2) The scale has an absolute zero point, which means that a score of "0" reflects a complete absence of whatever is being measured. Moreover, a ratio scale is the only scale of measurement that possesses the quality of an absolute zero point. This is true of percentage scores; a score of 0% indicates that the person answered no questions correctly. If you thought the answer was ordinal scale, you may have been confusing percentage scores with percentile ranks. examples of ordinal scores: ranks (e.g., 1st best, 2nd best, 3rd best), win/place/show, high/moderate/low, and attitude scales that ask people to choose between "strongly agree," "agree," "neutral," "disagree," and "strongly disagree" in response to a statement. examples of interval scores: IQ scores as well as scores on most standardized tests and temperature examples of ratio scores: dollar amounts; time as measured in seconds, minutes, or hours; measures of distance, height, weight; and frequency of behaviors per hour.

In the multitrait-multimethod matrix, a low heterotrait-heteromethod coefficient would indicate: Select one: A.low convergent validity B.low divergent validity C.high convergent validity D.high divergent validity

Correct Answer is: D Use of a multitrait-multimethod matrix is one method of assessing a test's construct validity. The matrix contains correlations among different tests that measure both the same and different traits using similar and different methodologies. The heterotrait-heteromethod coefficient, one of the correlation coefficients that would appear on this matrix, reflects the correlation between two tests that measure different (hetero) traits using different (hetero) methods. An example might be the correlation between vocabulary subtest scores on the WAIS-IV for intelligence and scores on the Beck Depression Inventory for depression. Since these measures presumably measure different constructs, the correlation coefficient should be low, indicating high divergent or discriminant validity.

In a factor analysis, an eigenvalue corresponds to Select one: A.the number of latent variables. B.the strength of the relationship between factors. C.the level of significance of the factor analysis. D.the explained variance of one of the factors.

Correct Answer is: D When a factor analysis produces a series of factors, it is useful to determine how much of the variance is accounted for by each factor. An eigenvalue is based on the factor loadings of all the variables in the factor analysis to a particular factor. When the factor loadings are high, the eigenvalue will be large. A large eigenvalue would mean that a particular factor accounts for a large proportion of the variance among the variables.

All of the following are assumptions of the regression equation, except: A.a linear relationship exists between X and Y. B.the variability of Y scores is equal throughout the range of X scores. C.one can predict scores on Y on the basis of scores on X. D.changes in the level of X cause changes in the level of Y.

Correct Answer is: DA regression equation is used to predict the value of a Y variable on the basis of a person's score on an X variable. For example, if an industrial psychologist wanted to use a job applicant's score on a job selection test to predict his future score on a supervisor's rating scale, he could develop and use a regression equation to do so. When regression is used, there is not necessarily the assumption that changes in the value of X cause changes in Y. The X and Y variables are correlated, but a correlation between two variables does not always mean that they are causally related. The incorrect choices are all assumptions of the use of regression. a linear relationship exists between X and Y. The technique is based on the assumption that the relationship between X and Y can be depicted as a straight line. the variability of Y scores is equal throughout the range of X scores. This choice is referred to as the assumption of homoscedasticity. one can predict scores on Y on the basis of scores on X. And this describes the whole purpose of using regression.

In qualitative research, which strategy seeks to illustrate a particular phenomenon? A.Critical case sampling. B.Network sampling. C.Comprehensive sampling. D.Maximum variation sampling.

Correct answer: A

In a positively skewed distribution, one would most likely find, ranked from lowest to highest in value, the: Select one: A.median, mean, mode. B.median, mode, mean. C.mean, mode, median. D.mode, median, mean.

Correct Answer is: DYou have to picture the positively skewed curve in order to get this correct. Positive skewness means there are some outliers (extreme scores) way over on the positive side. That's where the tail is, way off to the right, or positive, end. Since the mean takes into account the magnitude of the scores, these outliers can be pictured as "pulling" the mean to the positive side, or the right. So, in any ordering of measures of central tendency, the mean would be the highest value. Thus, you can eliminate the two distractors that don't list the mean as the highest value. To distinguish between the remaining answers, let's go back to consider what the median is. The median is the middlemost point irrespective of value. If you've pictured the curve correctly you can see that more than half the cases fall on the right side because some are way over on the positive side. If you put a line where the highest point is on the curve, which is the mode, you'd see that more than half the cases fall to the right of that line. Hence the median, the 50% point, is to the right of the high point, the mode. This should have gotten you to the correct answer.

A researcher wants to test her model of remarriage. What statistical technique should she use if her model includes multiple pathways involving multiple predictors and multiple criterion variables? 1. Structural equation modeling. 2. Hierarchical multiple regression. 3. MANOVA. 4. Trend analysis.

Correct Answer: 1. Feedback: Structural equation modeling (one type is known as LISREL) enables researchers to make inferences about causation. It can be used to test out many different causal pathways, involving multiple predictor and criterion variables. MANOVA (Response 3) involves one or more independent variables with at least two dependent variables; it is a test of group differences and would be inappropriate for testing a causal model. Similarly, trend analysis (Response 4) is also a test of group differences, used when the outcome data is non-linear. Although hierarchical multiple regressions (Response 2) are used to test theories, they can only include one criterion variable and do not test multiple pathways

In a single-subject research design, which of the following is the most significant problem? 1. Autocorrelation. 2. Multicollinearity. 3. Regression to the mean. 4. Practice effects.

Correct Answer: 1. Autocorrelation. Feedback: When the same subject is measured repeatedly, the measures demonstrate a high degree of correlation, which is termed autocorrelation. Autocorrelation can either appear to enhance or to decrease the effect of the independent variable. Multicollinearity (Response 2) is a problem in multiple regression analysis when the predictors are highly correlated. Response 3 and 4 are general threats to internal validity and are not relevant here. While single-subject designs do involve repeatedly measuring subjects, the measures are not always tests (e.g., the measure can be frequency of observed behavior); hence, practice effects (Response 4) are not always a problem.

The association between two variables, when each variable's association with another variable has been removed, is known as: • 1. analysis of covariance. • 2. partial correlation. • 3. semi-partial correlation. • 4. coefficient of determination.

Correct Answer: 2. partial correlation. Feedback: This is a tricky question that would be difficult to guess correctly. A partial correlation is the correlation ("association") between two variables when the association between a third variable and each of the two original variables has been partialed out ("removed"). In a semi-partial correlation (Response 3), the association with the third variable is partialed out for only one of the two initial variables. Analysis of Covariance or ANCOVA (Response 1) uses a similar process, but ANCOVAs, which are a variant of ANOVAs, examine the difference between groups whereas this question calls for a measure of association or correlation. Finally, the coefficient of determination (Response 4) is the term for the proportion of variance shared by two variables and is the square of the correlation coefficient.

When running an ANOVA, a pooled error term is justified when: 1. sample size is unequal. 2. variance is equal. 3. all cells have the same number of subjects. 4. homoscedasticity is violated.

Correct Answer: 2. variance is equal. Feedback: This is a difficult question, requiring an advanced understanding of statistics. It is best to simply know that a pooled error term is used when there is homogeneity of variance (i.e., the variance is equal). When variance is not equal, a separate error term should be used. Homoscedasticity also refers to equal variance. If it were violated (Response 4), the variance would not be equal and a pooled error term could not be used. A mnemonic here might be: when things are equal, they can be pooled together; when unequal, they must be treated separately. Sample size (Response 1) and the number of subjects per cell (Response 3) are not the determining factors in the use of a pooled error term.

Dr. Spook hypothesizes that "fear" involves three components: physical arousal, cognitive appraisal of danger, and suggestibility. He designs a self-report questionnaire that yields a quantitative measure of degree of fear. He includes questions addressing physical arousal, cognitive appraisal of danger, and suggestibility. To confirm his hypothesis, he should use which of the following statistics? • 1. Discriminant analysis. • 2. Path analysis. • 3. Factor analysis. • 4. Analysis of variance.

Correct Answer: 3. Factor analysis. Feedback: Dr. Spook's hypothesis is about the underlying structure of fear. Specifically, he hypothesizes three underlying components: physical arousal, cognitive appraisal of danger, and suggestibility. Factor analysis is a test of structure and would therefore be appropriate here. Other tests of structure include principle components analysis and cluster analysis. Discriminant analysis (Response 1) is used to predict group membership. Path analysis (Response 2) involves the application of correlational techniques to test models of causality. Analysis of variance (Response 4) looks at differences between two or more groups of subjects.

A researcher is studying the effect of different treatments for hyperactivity. First graders receive one of two treatments. Group A's pre-treatment mean is 15, and the post-treatment mean is 13. Group B's pre-treatment mean is 27, and the post-treatment mean is 24. The most likely threat to this research is: 1. maturation. 2. regression. 3. selection. 4. demand characteristics.

Correct Answer: 3. selection. Feedback: This research is most significantly affected by the threat of selection bias, or non-random assignment. Given that the pretreatment means are so dramatically different, it is unlikely that subjects were randomly assigned to the groups. Maturation (Response 1) and regression (Response 2) are typically more of concern when the study is a one-group pre-post design.